Статті в журналах: "COLLABORATIVE FILTERING ALGORITHMS"

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Ben Kharrat, Firas, Aymen Elkhleifi, and Rim Faiz. "Improving Collaborative Filtering Algorithms." International Journal of Knowledge Society Research 7, no. 3 (July 2016): 99–118. http://dx.doi.org/10.4018/ijksr.2016070107.

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This paper puts forward a new recommendation algorithm based on semantic analysis as well as new measurements. Like Facebook, Social network is considered as one of the most well-prominent Web 2.0 applications and relevant services elaborating into functional ways for sharing opinions. Thereupon, social network web sites have since become valuable data sources for opinion mining. This paper proposes to introduce an external resource a sentiment from comments posted by users in order to anticipate recommendation and also to lessen the cold-start problem. The originality of the suggested approach means that posts are not merely characterized by an opinion score, but receive an opinion grade notion in the post instead. In general, the authors' approach has been implemented with Java and Lenskit framework. The study resulted in two real data sets, namely MovieLens and TripAdvisor, in which the authors have shown positive results. They compared their algorithm to SVD and Slope One algorithms. They have fulfilled an amelioration of 10% in precision and recall along with an improvement of 12% in RMSE and nDCG.

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Cacheda, Fidel, Víctor Carneiro, Diego Fernández, and Vreixo Formoso. "Comparison of collaborative filtering algorithms." ACM Transactions on the Web 5, no. 1 (February 2011): 1–33. http://dx.doi.org/10.1145/1921591.1921593.

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3

Zhou, Li Juan, Ming Sheng Xu, and Hai Jun Geng. "Improved Attack-Resistant Collaborative Filtering Algorithm." Key Engineering Materials 460-461 (January 2011): 439–44. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.439.

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Collaborative filtering is very effective in recommendation systems. But the recently researches have proved the collaborative filtering is significant vulnerable in the face of profile injection attacks. Profile injection attacks can be identified to some attack models. The attacker can easily bias the prediction of the system based on collaborative filtering algorithms. In this paper, an improved algorithm based on Singular Value Decomposition is proposed. Some dimensions are chosen by the improved algorithm to find capture latent relationships between customers and products. In addition, the robustness of the algorithm is improved by the way. Several experiments are conducted. The results suggest that the proposed algorithm has advantages both in robust and stable over previous algorithms.

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4

Wu, Xinyi. "Comparison Between Collaborative Filtering and Content-Based Filtering." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 480–89. http://dx.doi.org/10.54097/hset.v16i.2627.

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With the rapid development of Internet technology nowadays, how to quickly obtain the effective information needed by users has become the key point of the scientific and technological academia. Therefore, various kinds of recommendation algorithms have been invented. Based on the previous research, this paper introduces the most famous and widely used recommendation algorithms among many recommendation systems, which are collaborative filtering and content-based filtering. In this paper, the core ideas and operation principles of the two algorithms are introduced in detail. In addition, by describing the steps of these two algorithms gradually and analyzing their processes step by step, we can accurately analyze and summarize their advantages and disadvantages respectively. And on this basis, the respective areas which they are good at are mentioned. Moreover, this paper points out the shortcomings and limitations that still exist at present, and the direction for further improvement in the future. Finally, at the end of the paper, there are some overall comparation and summation about the two algorithms. And the hot research points of them in the future are discussed.

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5

Jalili, Mahdi. "A Survey of Collaborative Filtering Recommender Algorithms and Their Evaluation Metrics." International Journal of System Modeling and Simulation 2, no. 2 (June 30, 2017): 14. http://dx.doi.org/10.24178/ijsms.2017.2.2.14.

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Abstract—Recommender systems are often used to provide useful recommendations for users. They use previous history of the users-items interactions, e.g. purchase history and/or users rating on items, to provide a suitable recommendation list for any target user. They may also use contextual information available about items and users. Collaborative filtering algorithm and its variants are the most successful recommendation algorithms that have been applied to many applications. Collaborative filtering method works by first finding the most similar users (or items) for a target user (or items), and then building the recommendation lists. There is no unique evaluation metric to assess the performance of recommendations systems, and one often choose the one most appropriate for the application in hand. This paper compares the performance of a number of well-known collaborative filtering algorithms on movie recommendation. To this end, a number of performance criteria are used to test the algorithms. The algorithms are ranked for each evaluation metric and a rank aggregation method is used to determine the wining algorithm. Our experiments show that the probabilistic matrix factorization has the top performance in this dataset, followed by item-based and user-based collaborative filtering. Non-negative matrix factorization and Slope 1 has the worst performance among the considered algorithms. Keywords—Social networks analysis and mining, big data, recommender systems, collaborative filtering.

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6

Zhang, Zhen, Taile Peng, and Ke Shen. "Overview of Collaborative Filtering Recommendation Algorithms." IOP Conference Series: Earth and Environmental Science 440 (March 19, 2020): 022063. http://dx.doi.org/10.1088/1755-1315/440/2/022063.

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7

Jing, Hui. "Application of Improved K-Means Algorithm in Collaborative Recommendation System." Journal of Applied Mathematics 2022 (December 22, 2022): 1–10. http://dx.doi.org/10.1155/2022/2213173.

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With the explosive growth of information resources in the age of big data, mankind has gradually fallen into a serious “information overload” situation. In the face of massive data, collaborative filtering algorithm plays an important role in information filtering and information refinement. However, the recommendation quality and efficiency of collaborative filtering recommendation algorithms are low. The research combines the improved artificial bee colony algorithm with K-means algorithm and applies them to the recommendation system to form a collaborative filtering recommendation algorithm. The experimental results show that the MAE value of the new fitness function is 0.767 on average, which has good separation and compactness in clustering effect. It shows high search accuracy and speed. Compared with the original collaborative filtering algorithm, the average absolute error value of this algorithm is low, and the running time is only 50 s. It improves the recommendation quality and ensures the recommendation efficiency, providing a new research path for the improvement of collaborative filtering recommendation algorithm.

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8

Jiang, Tong Qiang, and Wei Lu. "Improved Slope One Algorithm Based on Time Weight." Applied Mechanics and Materials 347-350 (August 2013): 2365–68. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2365.

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Collaborative filtering is regarded as the most prevailing techniques for recommendation system. Slope one is a family of algorithms used for collaborative filtering. It is the simplest form of non-trivial item-based collaborative filtering based on ratings. But all the family of use CF algorithms ignores one important problem: ratings produced at different times are weighted equally. It means that they cant catch users different attitudes at different time. So in this paper, we present a new algorithm, which could assign different weights for items at different time. Finally, we experimentally evaluate our approach and compare it to the original Slope One. The experiment shows that the new slope one algorithms can improve the precision

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9

Li, Xiaofeng, and Dong Li. "An Improved Collaborative Filtering Recommendation Algorithm and Recommendation Strategy." Mobile Information Systems 2019 (May 7, 2019): 1–11. http://dx.doi.org/10.1155/2019/3560968.

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The e-commerce recommendation system mainly includes content recommendation technology, collaborative filtering recommendation technology, and hybrid recommendation technology. The collaborative filtering recommendation technology is a successful application of personalized recommendation technology. However, due to the sparse data and cold start problems of the collaborative recommendation technology and the continuous expansion of data scale in e-commerce, the e-commerce recommendation system also faces many challenges. This paper has conducted useful exploration and research on the collaborative recommendation technology. Firstly, this paper proposed an improved collaborative filtering algorithm. Secondly, the community detection algorithm is investigated, and two overlapping community detection algorithms based on the central node and k-based faction are proposed, which effectively mine the community in the network. Finally, we select a part of user communities from the user network projected by the user-item network as the candidate neighboring user set for the target user, thereby reducing calculation time and increasing recommendation speed and accuracy of the recommendation system. This paper has a perfect combination of social network technology and collaborative filtering technology, which can greatly increase recommendation system performance. This paper used the MovieLens dataset to test two performance indexes which include MAE and RMSE. The experimental results show that the improved collaborative filtering algorithm is superior to other two collaborative recommendation algorithms for MAE and RMSE performance.

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10

Kourtiche, Ali, and Mohamed Merabet. "Collaborative Filtering Technical Comparison in Implicit Data." International Journal of Knowledge-Based Organizations 11, no. 4 (October 2021): 1–24. http://dx.doi.org/10.4018/ijkbo.2021100101.

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Recommendation systems have become a necessity due to the mass of information accumulated for each site. For this purpose, there are several methods including collaborative filtering and content-based filtering. For each approach there is a vast list of procedural choices. The work studies the different methods and algorithms in the field of collaborative filtering recommendation. The objective of the work is to implement these algorithms in order to compare the different performances of each one; the tests were carried out in two datasets, book crossing and Movieslens. The use of a data set benchmark is crucial for the proper evaluation of collaborative filtering algorithms in order to draw a conclusion on the performance of the algorithms.

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Jia, Likun, Yaohui Wang, Shiyu Yan, and Dan Xiao. "Analysis and research on the Algorithm of university book recommendation." Highlights in Science, Engineering and Technology 9 (September 30, 2022): 424–29. http://dx.doi.org/10.54097/hset.v9i.1874.

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This paper analyzes the performance of user-based collaborative filtering algorithm and item-based collaborative filtering algorithm in university library lending data and the applicable environment of different algorithms through accuracy and recall rate. The borrowing data of university library are processed by different methods and the processed data are run by different algorithms. The running results of different cutting ratios of training sets and test sets, different recommended quantities and different data processing are recorded. And then look for variables that are relevant. Finally, the best running environment of different recommendation algorithms is found by comparing the data.

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Song, Jiagang, Jiayu Song, Xinpan Yuan, Xiao He, and Xinghui Zhu. "Graph Representation-Based Deep Multi-View Semantic Similarity Learning Model for Recommendation." Future Internet 14, no. 2 (January 19, 2022): 32. http://dx.doi.org/10.3390/fi14020032.

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With the rapid development of Internet technology, how to mine and analyze massive amounts of network information to provide users with accurate and fast recommendation information has become a hot and difficult topic of joint research in industry and academia in recent years. One of the most widely used social network recommendation methods is collaborative filtering. However, traditional social network-based collaborative filtering algorithms will encounter problems such as low recommendation performance and cold start due to high data sparsity and uneven distribution. In addition, these collaborative filtering algorithms do not effectively consider the implicit trust relationship between users. To this end, this paper proposes a collaborative filtering recommendation algorithm based on graphsage (GraphSAGE-CF). The algorithm first uses graphsage to learn low-dimensional feature representations of the global and local structures of user nodes in social networks and then calculates the implicit trust relationship between users through the feature representations learned by graphsage. Finally, the comprehensive evaluation shows the scores of users and implicit users on related items and predicts the scores of users on target items. Experimental results on four open standard datasets show that our proposed graphsage-cf algorithm is superior to existing algorithms in RMSE and MAE.

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13

Niu, Yizhen. "Collaborative Filtering-Based Music Recommendation in Spark Architecture." Mathematical Problems in Engineering 2022 (May 12, 2022): 1–8. http://dx.doi.org/10.1155/2022/9050872.

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The use of recommendation algorithms to recommend music MOOC resources is a method that is gradually gaining ground in people’s lives along with the development of the Internet. The often used ALS collaborative filtering algorithm has an irreplaceable role in personalised recommender systems via the Spark MLlib platform. In the study, it is investigated how Spark can be used to implement efficient music system recommendations. The collaborative filtering algorithm based on the ALS model in the Spark architecture is currently the most widely used technique in recommendation algorithms, allowing for the analysis and optimisation of computational techniques. The project-based collaborative filtering algorithm used in the article enables the recommendation of music by avoiding personal information about the user. More accurate user recommendations are achieved by predicting the user’s preferences and focusing on the top ranked and highly preferred music recommendations. The method improves the performance of the recommendation algorithm, which is optimised by Spark shuffle on top of resource optimisation, and its performance improved by 54.8% after optimisation compared to when there is no optimisation.

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Dahiya, Prachi, and Neelam Duhan. "Comparative Analysis of Various Collaborative Filtering Algorithms." International Journal of Computer Sciences and Engineering 7, no. 8 (August 31, 2019): 347–51. http://dx.doi.org/10.26438/ijcse/v7i8.347351.

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15

Soojung Lee. "Clustering-based Collaborative Filtering Using Genetic Algorithms." Journal of Creative Information Culture 4, no. 3 (December 2018): 221–30. http://dx.doi.org/10.32823/jcic.4.3.201812.221.

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16

HUANG Tianshou, SHI Yongliang, HUANG Yiqun, and RONG Wei. "Personalization Services based on Collaborative Filtering Algorithms." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 3, no. 5 (June 30, 2011): 176–84. http://dx.doi.org/10.4156/aiss.vol3.issue5.21.

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17

Jalili, Mahdi, Sajad Ahmadian, Maliheh Izadi, Parham Moradi, and Mostafa Salehi. "Evaluating Collaborative Filtering Recommender Algorithms: A Survey." IEEE Access 6 (2018): 74003–24. http://dx.doi.org/10.1109/access.2018.2883742.

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18

Shirakawa, Takahisa, and Setsuya Kurahashi. "Personal classification space-based collaborative filtering algorithms." International Journal of Computer Applications in Technology 46, no. 1 (2013): 3. http://dx.doi.org/10.1504/ijcat.2013.051383.

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Refkrisnatta, Alldie, and Dewi Handayani. "Cafe Selection Recommendation System in Semarang City Uses Collaborative Filtering Method with Item Based Filtering Algorithm." JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) 6, no. 2 (October 19, 2022): 95–108. http://dx.doi.org/10.21070/jeeeu.v6i2.1637.

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The cafe selection recommendation system in the city of Semarang aims to provide recommendations for users in finding the desired café according to the type of café expected. This recommendation system serves to predict an item that is of interest to the user. Implementation of recommendation system using Collaborative Filtering and Item Based Filtering algorithms. Collaborative filtering is a recommendation system algorithm where recommendations are given based on consideration of data from other users while the Item Based Filtering algorithm to provide recommendations based on similarities between customer tastes and café characteristics.

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Refkrisnatta, Alldie, and Dewi Handayani. "Café Selection Recommendation System in Semarang City uses Collaborative Filtering Method with Item based Filtering Algorithm." JEEMECS (Journal of Electrical Engineering, Mechatronic and Computer Science) 6, no. 1 (February 27, 2023): 27–36. http://dx.doi.org/10.26905/jeemecs.v6i1.7446.

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The café selection recommendation system in the city of Semarang aims to provide recommendations for users in finding the desired café according to the type of café expected. This recommendation system serves to predict an item that is of interest to the user. Implementation of recommendation system using Collaborative Filtering and Item Based Filtering algorithms. Collaborative filtering is a recommendation system algorithm where recommendations are given based on consideration of data from other users while the Item Based Filtering algorithm to provide recommendations based on similarities between customer tastes and café characteristics.

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XUE, B. X., and T. LIU. "INVESTMENT DECISION OF TOURISM LEISURE PROJECT BASED ON COLLABORATIVE FILTERING ALGORITHM." Latin American Applied Research - An international journal 48, no. 4 (October 31, 2018): 293–97. http://dx.doi.org/10.52292/j.laar.2018.243.

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In the era of the Big Bang, users have to spend a lot of time looking for the information they really need, and the search engine can't present information that is not described by the user. Based on the User based collaborative filtering recommendation algorithm and the collaborative filtering recommendation algorithm, this paper proposes User-CF algorithm, User-CF-1 algorithm, Item-CF algorithm, Item-CF-1 algorithm, and finally integrates four algorithms to obtain the cooperative strategy based on collaborative filtering is a hybrid recommendation algorithm, namely the Final algorithm. It has been verified that the Final algorithm can effectively solve the long tail problem in the travel strategy recommendation and greatly increase the coverage of the recommended strategy.

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Tan, Jingwen, Youxin Hu, and Jianqiu Luo. "Social recommendation algorithm based on collaborative filter algorithms." Frontiers in Computing and Intelligent Systems 3, no. 1 (March 22, 2023): 120–23. http://dx.doi.org/10.54097/fcis.v3i1.6346.

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For traditional collaborative filter recommendation algorithm technology, this paper combines the collaborative filtering recommendation algorithm with the community division technology of social networks, use the Louvain community to divide algorithms, divide the recommendation users to a community of similar users, and use the collaborative filter algorithm based on the user similarity formula within the community to recommend. In order to verify the effectiveness and accuracy of the algorithm in this paper, based on the introduction of the Douban dataset and the evaluation criteria used, a variety of comparative experiments are carried out on the Douban dataset with a variety of recommendation algorithms to verify the effectiveness of the proposed algorithm

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Spiliotopoulos, Dimitris, Dionisis Margaris, and Costas Vassilakis. "On Exploiting Rating Prediction Accuracy Features in Dense Collaborative Filtering Datasets." Information 13, no. 9 (September 11, 2022): 428. http://dx.doi.org/10.3390/info13090428.

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One of the typical goals of collaborative filtering algorithms is to produce rating predictions with values very close to what real users would give to an item. Afterward, the items having the largest rating prediction values will be recommended to the users by the recommender system. Collaborative filtering algorithms can be applied to both sparse and dense datasets, and each of these dataset categories involves different kinds of risks. As far as the dense collaborative filtering datasets are concerned, where the rating prediction coverage is, most of the time, very high, we usually face large rating prediction times, issues concerning the selection of a user’s near neighbours, etc. Although collaborative filtering algorithms usually achieve better results when applied to dense datasets, there is still room for improvement, since in many cases, the rating prediction error is relatively high, which leads to unsuccessful recommendations and hence to recommender system unreliability. In this work, we explore rating prediction accuracy features, although in a broader context, in dense collaborative filtering datasets. We conduct an extensive evaluation, using dense datasets, widely used in collaborative filtering research, in order to find the associations between these features and the rating prediction accuracy.

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Yin, Nan. "A Big Data Analysis Method Based on Modified Collaborative Filtering Recommendation Algorithms." Open Physics 17, no. 1 (December 31, 2019): 966–74. http://dx.doi.org/10.1515/phys-2019-0102.

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Abstract With the rapid development of e-commerce, collaborative filtering recommendation system has been widely used in various network platforms. Using recommendation system to accurately predict customers’ preferences for goods can solve the problem of information overload faced by users and improve users’ dependence on the network platform. Because the recommendation system based on collaborative filtering technology has the ability to recommend more abstract or difficult to describe goods in words, the research related to collaborative filtering technology has attracted more and more attention. According to the past research, in collaborative filtering algorithm, if Pearson correlation coefficient is used, errors will occur under special circumstances. In this study, the normal recovery similarity measure is used to modify the similarity value to correct the error value of a collaborative filtering recommendation algorithm. Based on this, a big data analysis method based on a modified collaborative filtering recommendation algorithm is proposed. This research implemented it in the cloud Hadoop environment, and measure the execution time with 2, 5 and 8 nodes. Then the research compared it with the execution time of a single machine, and analyze its speedup ratio and efficiency. The experimental results show that the execution time increases with the number of neighbors. When the number of nodes is 5 and 8, the execution time is greatly improved, which improves the efficiency of collaborative filtering algorithm and can cope with massive data in the future.

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Cheng, Jiujun, Yingbo Liu, Huiting Zhang, Xiao Wu, and Fuzhen Chen. "A New Recommendation Algorithm Based on User’s Dynamic Information in Complex Social Network." Mathematical Problems in Engineering 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/281629.

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Анотація:

The development of recommendation system comes with the research of data sparsity, cold start, scalability, and privacy protection problems. Even though many papers proposed different improved recommendation algorithms to solve those problems, there is still plenty of room for improvement. In the complex social network, we can take full advantage of dynamic information such as user’s hobby, social relationship, and historical log to improve the performance of recommendation system. In this paper, we proposed a new recommendation algorithm which is based on social user’s dynamic information to solve the cold start problem of traditional collaborative filtering algorithm and also considered the dynamic factors. The algorithm takes user’s response information, dynamic interest, and the classic similar measurement of collaborative filtering algorithm into account. Then, we compared the new proposed recommendation algorithm with the traditional user based collaborative filtering algorithm and also presented some of the findings from experiment. The results of experiment demonstrate that the new proposed algorithm has a better recommended performance than the collaborative filtering algorithm in cold start scenario.

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Wang, Song, Cheng Zeng, Bailing Song, Xuxiang Huang, and Sicheng Zhou. "Research on the Spectral Domain Graph Convolution Collaborative Filtering Algorithm Based on Reinforcement Learning and Chebyshev." Wireless Communications and Mobile Computing 2022 (August 8, 2022): 1–11. http://dx.doi.org/10.1155/2022/7787633.

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Анотація:

To solve the abovementioned problem, we propose a collaborative filtering recommendation algorithm that incorporates singular value decomposition (SVD) and Chebyshev truncation in spectral domain convolution. Firstly, the SVD algorithm is used to optimize the adjacency matrix, mine the potential association information between users and items, and expand the user-item adjacency matrix. Finally, based on the MovieLens-1M public dataset, the proposed algorithm (CBSVD-SCF) is compared with other commonly used algorithms. The results show that the article optimizes the recommendation effect of the algorithm based on the traditional collaborative filtering algorithm by combining the temporal order and sequence of user interaction information, as well as the popularity of items and the activity of users; the experimental results on MovieLens show that the optimized collaborative filtering recommendation algorithm can effectively improve the recommendation effect.

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Rao, P. Rama. "Movie Recommending System Using Collaborative Filtering." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 15, 2021): 1034–38. http://dx.doi.org/10.22214/ijraset.2021.36377.

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Movies are one of the sources of entertainment, but the problem is in finding the content of our choice because content is increasing every year. However, recommendation systems plays here an important role for finding the content of desired domain in these situations. The aim of this paper is to improve the accuracy and performance of a filtration techniques existed. There are several methods and algorithms existed to implement a recommendation system. Content-based filtering is the simplest method, it takes input from the users, checks the movie and its content and recommends a list of similar movies. In this paper, to prove the effectiveness of our system, K-NN algorithms and collaborative filtering are used. Here, the usage of cosine similarity is done for recommending the nearest neighbours.

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Fernández, Diego, Francisco J. Nóvoa, Fidel Cacheda, and Víctor Carneiro. "Advancing Network Flow Information Using Collaborative Filtering." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, Suppl. 2 (December 2017): 97–112. http://dx.doi.org/10.1142/s021848851740013x.

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Анотація:

Collaborative Filtering algorithms are frequently employed in e-commerce. However, this kind of algorithms can also be useful in other domains. In an information system thousands of bytes are sent through the network every second. Analyzing this data can require too much time and many resources, but it is necessary for ensuring the right operation of the network. Results are used for profiling, security analysis, traffic engineering and many other purposes. Nowadays, as a complement to a deep inspection of the data, it is more and more common to monitor packet flows, since it consumes less resources and it allows to react faster to any network situation. In a typical ow monitoring system, flows are exported to a collector, which stores the information before being analyzed. However, many collectors work based on time slots, so they do not analyze the flows when they are just received, generating a delay. In this work we demonstrate how Collaborative Filtering algorithms can be applied to this new domain. In particular, using information about past flows, these algorithms can anticipate future flows before being captured. This way, time required for detecting and responding to different network situations is reduced.

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Huang, Gang, Man Yuan, Chun-Sheng Li, and Yong-he Wei. "Personalized Knowledge Recommendation Based on Knowledge Graph in Petroleum Exploration and Development." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 10 (January 28, 2020): 2059033. http://dx.doi.org/10.1142/s0218001420590338.

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Анотація:

Firstly, this paper designs the process of personalized recommendation method based on knowledge graph, and constructs user interest model. Second, the traditional personalized recommendation algorithms are studied and their advantages and disadvantages are analyzed. Finally, this paper focuses on the combination of knowledge graph and collaborative filtering recommendation algorithm. They are effective to solve the problem where [Formula: see text] value is difficult to be determined in the clustering process of traditional collaborative filtering recommendation algorithm as well as data sparsity and cold start, utilizing the ample semantic relation in knowledge graph. If we use RDF data, which is distributed by the E and P (Exploration and Development) database based on the petroleum E and P, to verify the validity of the algorithm, the result shows that collaborative filtering algorithm based on knowledge graph can build the users’ potential intentions by knowledge graph. It is enlightening to query the information of users. In this way, it expands the mind of users to accomplish the goal of recommendation. In this paper, a collaborative filtering algorithm based on domain knowledge atlas is proposed. By using knowledge graph to effectively classify and describe domain knowledge, the problems are solved including clustering and the cold start in traditional collaborative filtering recommendation algorithm. The better recommendation effect has been achieved.

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Su, Xiaoyuan, and Taghi M. Khoshgoftaar. "A Survey of Collaborative Filtering Techniques." Advances in Artificial Intelligence 2009 (October 27, 2009): 1–19. http://dx.doi.org/10.1155/2009/421425.

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Анотація:

As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, model-based, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.

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Sun, Ming Yang, Wei Feng Sun, Xi Dong Liu, and Lei Xue. "A Novel Personalized Filtering Recommendation Algorithm Based on Collaborative Tagging." Advanced Materials Research 186 (January 2011): 621–25. http://dx.doi.org/10.4028/www.scientific.net/amr.186.621.

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Анотація:

Recommendation algorithms suffer the quality from the huge and sparse dataset. Memory-based collaborative filtering method has addressed the problem of sparsity by predicting unrated values. However, this method increases the computational complexity, sparsity and expensive complexity of computation are trade-off. In this paper, we propose a novel personalized filtering (PF) recommendation algorithm based on collaborative tagging, which weights the feature of tags that show latent personal interests and constructs a top-N tags set to filter out the undersized and dense dataset. The PF recommendation algorithm can track the changes of personal interests, which is an untilled field for previous studies. The results of empirical experiments show that the sparsity level of PF recommendation algorithm is much lower, and it is more computationally economic than previous algorithms.

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Yang, Jing, Xiaoye Li, Zhenlong Sun, and Jianpei Zhang. "A Differential Privacy Framework for Collaborative Filtering." Mathematical Problems in Engineering 2019 (January 9, 2019): 1–11. http://dx.doi.org/10.1155/2019/1460234.

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Focusing on the privacy issues in recommender systems, we propose a framework containing two perturbation methods for differentially private collaborative filtering to prevent the threat of inference attacks against users. To conceal individual ratings and provide valuable predictions, we consider some representative algorithms to calculate the predicted scores and provide specific solutions for adding Laplace noise. The DPI (Differentially Private Input) method perturbs the original ratings, which can be followed by any recommendation algorithms. By contrast, the DPM (Differentially Private Manner) method is based on the original ratings, which perturbs the measurements during implementation of the algorithms and releases the predicted scores. The experimental results showed that both methods can provide valuable prediction results while guaranteeing DP, which suggests it is a feasible solution and can be competent to make private recommendations.

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33

Zhao, Yongxia, and Bo Zhang. "Research on Recommendation Algorithms Based on Collaborative Filtering." Journal of Physics: Conference Series 1237 (June 2019): 022094. http://dx.doi.org/10.1088/1742-6596/1237/2/022094.

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34

Zhang, Feng, Ti Gong, Victor E. Lee, Gansen Zhao, Chunming Rong, and Guangzhi Qu. "Fast algorithms to evaluate collaborative filtering recommender systems." Knowledge-Based Systems 96 (March 2016): 96–103. http://dx.doi.org/10.1016/j.knosys.2015.12.025.

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35

Yan, Xuechao, Shuhan Qi, and Chang Chen. "Recommender Systems: Collaborative Filtering and Content-based Recommender System." Applied and Computational Engineering 2, no. 1 (March 22, 2023): 346–51. http://dx.doi.org/10.54254/2755-2721/2/20220658.

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There are three algorithms of recommender systems proposed by this paper, which are item collaborative filtering(itemCF), user collaborative filtering(useCF) and content-based recommender system(CBRS). The principal goal of this paper is to try to ascertain which algorithm has the highest precision, after training based on the same dataset. In accordance with the data we chose and ceaseless testing, we observe itemCF contains the most accurate rate. However, we theoretically and empirically conceive each algorithm owns different advantages and drawbacks, should be used in the specific circumstance.

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36

Kim, Jihie, Jaebong Yoo, Ho Lim, Huida Qiu, Zornitsa Kozareva, and Aram Galstyan. "Sentiment Prediction Using Collaborative Filtering." Proceedings of the International AAAI Conference on Web and Social Media 7, no. 1 (August 3, 2021): 685–88. http://dx.doi.org/10.1609/icwsm.v7i1.14461.

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Learning sentiment models from short texts such as tweets is a notoriously challenging problem due to very strong noise and data sparsity. This paper presents a novel, collaborative filtering-based approach for sentiment prediction in twitter conversation threads. Given a set of sentiment holders and sentiment targets, we assume we know the true sentiments for a small fraction of holder-target pairs. This information is then used to predict the sentiment of a previously unknown user towards another user or an entity using collaborative filtering algorithms. We validate our model on two Twitter datasets using different collaborative filtering techniques. Our preliminary results demonstrate that the proposed approach can be effectively used in twitter sentiment prediction, thus mitigating the data sparsity problem.

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37

Guo, Lin, and Qin Ke Peng. "A Combinative Similarity Computing Measure for Collaborative Filtering." Applied Mechanics and Materials 347-350 (August 2013): 2919–25. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2919.

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Similarity method is the key of the user-based collaborative filtering recommend algorithm. The traditional similarity measures, which cosine similarity, adjusted cosine similarity and Pearson correlation similarity are included, have some advantages such as simple, easy and fast, but with the sparse dataset they may lead to bad recommendation quality. In this article, we first research how the recommendation qualities using the three similarity methods respectively change with the different sparse datasets, and then propose a combinative similarity measure considering the account of items users co-rated. Compared with the three algorithms, our method shows its satisfactory performance with the same computation complexity.

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38

Chen, Hailong, Haijiao Sun, Miao Cheng, and Wuyue Yan. "A Recommendation Approach for Rating Prediction Based on User Interest and Trust Value." Computational Intelligence and Neuroscience 2021 (March 6, 2021): 1–9. http://dx.doi.org/10.1155/2021/6677920.

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Collaborative filtering recommendation algorithm is one of the most researched and widely used recommendation algorithms in personalized recommendation systems. Aiming at the problem of data sparsity existing in the traditional collaborative filtering recommendation algorithm, which leads to inaccurate recommendation accuracy and low recommendation efficiency, an improved collaborative filtering algorithm is proposed in this paper. The algorithm is improved in the following three aspects: firstly, considering that the traditional scoring similarity calculation excessively relies on the common scoring items, the Bhattacharyya similarity calculation is introduced into the traditional calculation formula; secondly, the trust weight is added to accurately calculate the direct trust value and the trust transfer mechanism is introduced to calculate the indirect trust value between users; finally, the user similarity and user trust are integrated, and the prediction result is generated by the trust weighting method. Experiments show that the proposed algorithm can effectively improve the prediction accuracy of recommendations.

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39

Zhang, Yiman. "The application of e-commerce recommendation system in smart cities based on big data and cloud computing." Computer Science and Information Systems, no. 00 (2021): 26. http://dx.doi.org/10.2298/csis200917026z.

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In the era of big data, the amount of Internet data is growing explosively. How to quickly obtain valuable information from massive data has become a challenging task. To effectively solve the problems faced by recommendation technology, such as data sparsity, scalability, and real-time recommendation, a personalized recommendation algorithm for e-commerce based on Hadoop is designed aiming at the problems in collaborative filtering recommendation algorithm. Hadoop cloud computing platform has powerful computing and storage capabilities, which are used to improve the collaborative filtering recommendation algorithm based on project, and establish a comprehensive evaluation system. The effectiveness of the proposed personalized recommendation algorithm is further verified through the analysis and comparison with some traditional collaborative filtering algorithms. The experimental results show that the e-commerce system based on cloud computing technology effectively improves the support of various recommendation algorithms in the system environment; the algorithm has good scalability and recommendation efficiency in the distributed cluster, and the recommendation accuracy is also improved, which can improve the sparsity, scalability and real-time problems in e-commerce personalized recommendation. This study greatly improves the recommendation performance of e-commerce, effectively solves the shortcomings of the current recommendation algorithm, and further promotes the personalized development of e-commerce.

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40

Margaris, Dionisis, Dimitris Spiliotopoulos, Gregory Karagiorgos, and Costas Vassilakis. "An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings." Algorithms 13, no. 7 (July 17, 2020): 174. http://dx.doi.org/10.3390/a13070174.

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Collaborative filtering algorithms formulate personalized recommendations for a user, first by analysing already entered ratings to identify other users with similar tastes to the user (termed as near neighbours), and then using the opinions of the near neighbours to predict which items the target user would like. However, in sparse datasets, too few near neighbours can be identified, resulting in low accuracy predictions and even a total inability to formulate personalized predictions. This paper addresses the sparsity problem by presenting an algorithm that uses robust predictions, that is predictions deemed as highly probable to be accurate, as derived ratings. Thus, the density of sparse datasets increases, and improved rating prediction coverage and accuracy are achieved. The proposed algorithm, termed as CFDR, is extensively evaluated using (1) seven widely-used collaborative filtering datasets, (2) the two most widely-used correlation metrics in collaborative filtering research, namely the Pearson correlation coefficient and the cosine similarity, and (3) the two most widely-used error metrics in collaborative filtering, namely the mean absolute error and the root mean square error. The evaluation results show that, by successfully increasing the density of the datasets, the capacity of collaborative filtering systems to formulate personalized and accurate recommendations is considerably improved.

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Liu, Zhen, Huanyu Meng, Shuang Ren, and Feng Liu. "Reliable Collaborative Filtering on Spatio-Temporal Privacy Data." Security and Communication Networks 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/9127612.

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Lots of multilayer information, such as the spatio-temporal privacy check-in data, is accumulated in the location-based social network (LBSN). When using the collaborative filtering algorithm for LBSN location recommendation, one of the core issues is how to improve recommendation performance by combining the traditional algorithm with the multilayer information. The existing approaches of collaborative filtering use only the sparse user-item rating matrix. It entails high computational complexity and inaccurate results. A novel collaborative filtering-based location recommendation algorithm called LGP-CF, which takes spatio-temporal privacy information into account, is proposed in this paper. By mining the users check-in behavior pattern, the dataset is segmented semantically to reduce the data size that needs to be computed. Then the clustering algorithm is used to obtain and narrow the set of similar users. User-location bipartite graph is modeled using the filtered similar user set. Then LGP-CF can quickly locate the location and trajectory of users through message propagation and aggregation over the graph. Through calculating users similarity by spatio-temporal privacy data on the graph, we can finally calculate the rating of recommendable locations. Experiments results on the physical clusters indicate that compared with the existing algorithms, the proposed LGP-CF algorithm can make recommendations more accurately.

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42

Huang, Wenjun, Junyu Chen, and Yue Ding. "Research on Collaborative Filtering Recommendation Based on Trust Relationship and Rating Trust." Frontiers in Business, Economics and Management 1, no. 2 (April 19, 2021): 1–9. http://dx.doi.org/10.54097/fbem.v1i2.13.

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In the Internet age, how to dig out useful information from massive data has become a research hotspot. The emergence of recommendation algorithms effectively solves the problem of information overload, but traditional recommendation algorithms face problems such as data sparseness, cold start, and low accuracy. Later social recommendation algorithms usually only use a single social trust information for recommendation, and the integration of multiple trust relationships lacks an efficient model, which greatly affects the accuracy and reliability of recommendation. This paper proposes a trust-based approach. Recommended algorithm. First, use social trust data to calculate user trust relationships, including user local trust and user global trust. Further based on the scoring data, an implicit trust relationship is calculated, called rating trust, which includes scoring local trust and scoring global trust. Then set the recommendation weight, build the preference relationship between users through user trust and rating trust, and form a comprehensive trust relationship. The trust relationship of social networks is integrated into the probability matrix decomposition model to form an efficient and unified trusted recommendation model TR-PMF. This algorithm is compared with related algorithms on the Ciao and FilmTrust datasets, and the results prove that our method is competitive with other recommendation algorithms.

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Sun, Jinyang, Baisong Liu, Hao Ren, and Weiming Huang. "NCGAN:A neural adversarial collaborative filtering for recommender system." Journal of Intelligent & Fuzzy Systems 42, no. 4 (March 4, 2022): 2915–23. http://dx.doi.org/10.3233/jifs-210123.

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The major challenge of recommendation system (RS) based on implict feedback is to accurately model users’ preferences from their historical feedback. Nowadays, researchers has tried to apply adversarial technique in RS, which had presented successful results in various domains. To a certain extent, the use of adversarial technique improves the modeling of users’ preferences. Nonetheless, there are still many problems to be solved, such as insufficient representation and low-level interaction. In this paper, we propose a recommendation algorithm NCGAN which combines neural collaborative filtering and generative adversarial network (GAN). We use the neural networks to extract users’ non-linear characteristics. At the same time, we integrate the GAN framework to guide the recommendation model training. Among them, the generator aims to make user recommendations and the discriminator is equivalent to a measurement tool which could measure the distance between the generated distribution and users’ ground distribution. Through comparison with other existing recommendation algorithms, our algorithm show better experimental performance in all indicators.

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44

Margaris, Dionisis, Costas Vassilakis, and Dimitris Spiliotopoulos. "On Producing Accurate Rating Predictions in Sparse Collaborative Filtering Datasets." Information 13, no. 6 (June 15, 2022): 302. http://dx.doi.org/10.3390/info13060302.

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The typical goal of a collaborative filtering algorithm is the minimisation of the deviation between rating predictions and factual user ratings so that the recommender system offers suggestions for appropriate items, achieving a higher prediction value. The datasets on which collaborative filtering algorithms are applied vary in terms of sparsity, i.e., regarding the percentage of empty cells in the user–item rating matrices. Sparsity is an important factor affecting rating prediction accuracy, since research has proven that collaborative filtering over sparse datasets exhibits a lower accuracy. The present work aims to explore, in a broader context, the factors related to rating prediction accuracy in sparse collaborative filtering datasets, indicating that recommending the items that simply achieve higher prediction values than others, without considering other factors, in some cases, can reduce recommendation accuracy and negatively affect the recommender system’s success. An extensive evaluation is conducted using sparse collaborative filtering datasets. It is found that the number of near neighbours used for the prediction formulation, the rating average of the user for whom the prediction is generated and the rating average of the item concerning the prediction can indicate, in many cases, whether the rating prediction produced is reliable or not.

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45

Zhang, Yang, Guo Shun Zhou, and Shen Hua. "Evaluation of User-Based Collaborative Filtering Algorithms Using Regression." Applied Mechanics and Materials 201-202 (October 2012): 400–404. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.400.

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The explosive growth of world-wide-web and the emergence of e-commerce make the Collaborative Filtering (CF) algorithms widely used in recommender systems to identify a set of items that will be of interest for a certain user. However, with the rapid growth in the number of users and items, the quality of recommendation decreases. Improved CF algorithms are needed to provide a large number of users with the ability to efficiently obtain useful information. Different user-based approaches for computing similarities (e.g., correlation-based vs. adjusted-cosine) and different techniques for obtaining recommendations (e.g., weighted sum vs. regression model) are presented in the paper. The main steps of the algorithms are divided into two steps. The first is pre-computation of the similarities and pre-selecting of the N-nearest neighbors in the training phase, and the second is online prediction computation. All the algorithms require time linear in the number of items for online predicting. The experiments suggest that the user-based regression algorithms provide better qualities than those not using regression, and the average MAE drop is by 11.16%. Furthermore, the correlation-based regression gets the best MAE drop by 19.41%, and it provides better quality than the best available classic Pearson algorithms.

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46

Xu, Min, Mei Qi Fang, Pan Pan Yang, and Yu Chen. "Research on Personalized Learning Service Based on Collaborative Filtering Method." Advanced Materials Research 159 (December 2010): 252–57. http://dx.doi.org/10.4028/www.scientific.net/amr.159.252.

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In this paper, we discussed various personalized learning recommendation service and their advantages and disadvantages. On the basis of these methods, we proposed the similarity degree computing algorithm and user community discover algorithm. After verifying, analyzing and evaluating these algorithms and the recommendation model, we applied it as a recommendation service in SGCL (Social Group Collaborative Learning) System. Using the model in SGCL system, the system can recommend user personalized information and practical data proves that it can improve the learning quality effectively.

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47

Kumar Ojha, Rajesh, and Dr Bhagirathi Nayak. "Application of Machine Learning in Collaborative Filtering Recommender Systems." International Journal of Engineering & Technology 7, no. 4.38 (December 3, 2018): 213. http://dx.doi.org/10.14419/ijet.v7i4.38.24445.

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Recommender systems are one of the important methodologies in machine learning technologies, which is using in current business scenario. This article proposes a book recommender system using deep learning technique and k-Nearest Neighbors (k-NN) classification. Deep learning technique is one of the most effective techniques in the field of recommender systems. Recommender systems are intelligent systems in Machine Learning that can make difference from other algorithms. This article considers application of Machine Learning Technology and we present an approach based a recommender system. We used k-Nearest Neighbors classification algorithm of deep learning technique to classify users based book recommender system. We analyze the traditional collaborative filtering with our methodology and also to compare with them. Our outcomes display the projected algorithm is more precise over the existing algorithm, it also consumes less time and reliable than the existing methods.

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Wu, Ziling, Shi Wang, and Yuanhang Cai. "Data Mining Method of Intelligent Market Management Based on Collaborative Recommendation Algorithm." Computational Intelligence and Neuroscience 2022 (September 13, 2022): 1–12. http://dx.doi.org/10.1155/2022/8561567.

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In order to solve the problems of low coverage and accuracy and large mean absolute error and root mean square error when traditional algorithms recommend market management data, this paper proposes an intelligent market management data mining method based on a collaborative recommendation algorithm. According to the preference value of the attribute characteristics of market management data, predict and score the attribute characteristics of market management data; use data mining technology to preprocess the information of market management data, combined with the design of collaborative filtering recommendation algorithm; and realize the collaborative filtering recommendation of market management data. With 50 recommendations, AGCAN improves the accuracy of MovieLens-1M by 43.81%, 5.43%, 1.87%, 0.42%, and 1.67%, respectively, compared with the five benchmark algorithms. For MovieLens-100K, compared with the five benchmark algorithms, AGCAN improves the accuracy by 51.17%, 10.52%, 3.37%, 0.1%, and 0.30%, respectively. Compared with the five benchmark algorithms, Amazon-baby and AGCAN have improved the accuracy by 34.37%, 28.12%, 31.25%, 29.1%, and 3.12%, respectively. The algorithm proposed in this paper uses a graph neural network to mine useful information between users and projects, but it lacks the use of other personalized interest information of users, such as user interest, user purchase time, and so on.

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Ahmed, Esmael, and Adane Letta. "Book Recommendation Using Collaborative Filtering Algorithm." Applied Computational Intelligence and Soft Computing 2023 (March 11, 2023): 1–12. http://dx.doi.org/10.1155/2023/1514801.

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The explosive growth in the amount of available digital information in higher education has created a potential challenge of information overload, which hampers timely access to items of interest. The recommender systems are applied in different domains such as recommendations film, tourist advising, webpages, news, songs, and products. But the recommender systems pay less attention to university library services. The most users of university library are students. These users have a lack of ability to search and select the appropriate materials from the large repository that meet for their needs. A lot of work has been done on recommender system, but there are technical gaps observed in existing works such as the problem of constant item list in using web usage mining, decision tree induction, and association rule mining. Besides, it is observed that there is cold start problem in case-based reasoning approach. Therefore, this research work presents matrix factorization collaborative filtering with some performance enhancement to overcome cold start problem. In addition, it presents a comparative study among memory-based and model-based approaches. In this study, researchers used design science research method. The study dataset, 5189 records and 76,888 ratings, was collected from the University of Gondar student information system and online catalogue system. To develop the proposed model, memory-based and model-based approaches have been tested. In memory-based approach, matrix factorization collaborative filtering with some performance enhancements has been implemented. In model-based approach, K-nearest neighbour (KNN) and singular value decomposition (SVD) algorithms are also assessed experimentally. The SVD model is trained on our dataset optimized with a scored RMSE 0.1623 compared to RMSE 0.1991 before the optimization. The RMSE for a KNN model trained using the same dataset was 1.0535. This indicates that the matrix factorization performs better than KNN models in building collaborative filtering recommenders. The proposed SVD-based model accuracy score is 85%. The accuracy score of KNN model is 53%. So, the comparative study indicates that matrix factorization technique, specifically SVD algorithm, outperforms over neighbourhood-based recommenders. Moreover, using hyperparameter tuning with SVD also has an improvement on model performance compared with the existing SVD algorithm.

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Chen, Hong, Ming Xin Gan, and Meng Zhao Song. "An Improved Recommendation Algorithm Based on Graph Model." Applied Mechanics and Materials 380-384 (August 2013): 1266–69. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1266.

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According to the problem that the traditional search algorithms dont consider the needs of individuals, various recommender systems employing different data representations and recommendation methods are currently used to cope with these challenges. In this paper, inspired by the network-based user-item rating matrix, we introduce an improved algorithm which combines the similarity of items with a dynamic resource allocation process. To demonstrate its accuracy and usefulness, this paper compares the proposed algorithm with collaborative filtering algorithm using data from MovieLens. The evaluation shows that, the improved recommendation algorithm based on graph model achieves more accurate predictions and more reasonable recommendation than collaborative filtering algorithm or the basic graph model algorithm does.

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